ceRNA network construction and comparison of gastric cancer with or without Helicobacter pylori infection

Identification and Verification of a Glycolysis-Related lncRNA Prognostic Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

Hijacking and rewiring of host CircRNA/miRNA/mRNA competitive endogenous RNA (ceRNA) regulatory networks by oncoviruses during development of viral cancers

A significant portion of human cancers are caused by oncoviruses (12%-25%). Oncoviruses employ various strategies to promote their replication and induce tumourigenesis in host cells, one of which involves modifying the gene expression patterns of the host cells, leading to the rewiring of genes and resulting in significant changes in cellular processes and signalling pathways. In recent studies, a specific mode of gene regulation known as circular RNA (circRNA)-mediated competing endogenous RNA (ceRNA) networks has emerged as a key player in this context. CircRNAs, a class of non-coding RNA molecules, can interact with other RNA molecules, such as mRNAs and microRNAs (miRNAs), through a process known as ceRNA crosstalk. This interaction occurs when circRNAs, acting as sponges, sequester miRNAs, thereby preventing them from binding to their target mRNAs and modulating their expression. By rewiring the host cell genome, oncoviruses have the ability to manipulate the expression and activity of circRNAs, thereby influencing the ceRNA networks that can profoundly impact cellular processes such as cell proliferation, differentiation, apoptosis, and immune responses. This review focuses on a comprehensive evaluation of the latest findings on the involvement of virus-induced reprogramming of host circRNA-mediated ceRNA networks in the development and pathophysiology of human viral cancers, including cervical cancer, gastric cancer, nasopharyngeal carcinoma, Kaposi's sarcoma, hepatocellular carcinoma, and diffuse large B cell lymphoma. Understanding these mechanisms can improve our knowledge of how oncoviruses contribute to human tumourigenesis and identify potential targets for developing optimised therapies and diagnostic tools for viral cancers.

LINC01579-204 involved in the development of Hirschsprung's disease maybe by regulating the expression of miR-203a-3p and NEFL

BACKGROUND

Hirschsprung's disease (HD) is a rare congenital digestive tract malformation in children. Roles of long non-coding RNAs (lncRNAs) are highlighted in various human diseases. However, knowledge on lncRNAs in HD is still limited.

METHODS

The profile of lncRNAs in 8 pairs of normal and stenosed intestinal tissue of HD patients were obtained using microarray analysis. Base on bioinformatics analysis, the level of selected LINC01579-204, NEFL and miR-203a-3p was detected by qRT-PCR in 36 pairs of normal and stenosed intestinal tissue of HD patients. Then the predictive accuracy of LINC01579-204, miR-203a-3p and NEFL level to evaluate the progression of HD patients was analyzed with receiver operating characteristic curve (ROC).

RESULTS

A total of 90 differentially expressed lncRNAs were detected in normal and stenosed intestinal tissue of HD patients (|fold change| ≥ 1.5, p < 0.05). The level of LINC01579-204 and NEFL decreased and miR-203a-3p increased significantly in 36 pairs of stenosed intestinal tissue of HD patients compared to the control. A notable positive correlation was identified between LINC01579-204 and NEFL (r = 0.9681, p < 0.0001). Areas under the ROC curve of the LINC01579-204, miR-203a-3p and NEFL signature were 0.715, 0.777 and 0.829, respectively.

CONCLUSIONS

LINC01579-204, miR-203a-3p, and NEFL are predicted to play important roles in the progression of HD. LINC01579-204, miR-203a-3p and NEFL had a significant overall predictive ability to identify progression of HD patients. The novel experimental and bioinformatic results achieved in this study may provide new insights into the molecular of HD.

Identification of characteristic genes and construction of regulatory network in gallbladder carcinoma

BACKGROUND

Gallbladder carcinoma (GBC) is a highly malignant tumor with a poor overall prognosis. This study aimed to identify the characteristic microRNAs (miRNAs) of GBC and the competing endogenous RNA (ceRNA) regulatory mechanisms.

METHODS

The microarray data of GBC tissue samples and normal gallbladder (NGB) tissue samples from the Gene Expression Omnibus (GEO) database was downloaded. GBC-related differentially expressed miRNAs (DE-miRNAs) were identified by inter-group differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were used to screen the characteristic miRNA based on the intersect between least absolute shrinkage and selection operator (LASSO) and Support vector machine-recursive feature elimination (SVM-RFE). Based on the differential expression analysis of GEO database, the ceRNA network of characteristic miRNA was predicted and constructed. The biological functions of the ceRNA network were revealed by carrying out the gene enrichment analysis was implemented. We further screened the key genes of ceRNA network and constructed a protein-protein interaction (PPI) network, and predicted and generated the transcription factors (TFs) network of signature miRNAs. The expression of characteristic miRNA in clinical samples was verified by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 131 GBC-related DE-miRNAs were obtained. The hsa-miR-4770 was defined as characteristic miRNA for GBC. The ceRNA network containing 211 mRNAs, one miRNA, two lncRNAs, and 48 circRNAs was created. Gene enrichment analysis suggested that the downstream genes were mainly involved in actin filament organization, cell-substrate adhesion, cell-matrix adhesion, reactive oxygen species metabolic process, glutamine metabolic process and extracellular matrix (ECM)-receptor interaction pathway. 10 key genes in the network were found to be most correlated with disease, and involved in cell cycle-related processes, p53, and extrinsic apoptotic signaling pathways. The qRT-PCR result demonstrated that hsa-miR-4770 is down-regulated in GBC, and the expression trend is consistent with the public database.

CONCLUSIONS

We identified hsa-miR-4770 as the characteristic miRNA for GBC. The ceRNA network of hsa-miR-4770 may play key roles in GBC. This study provided some basis for potential pathogenesis of GBC.

FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

BACKGROUND

The circular RNA (circRNA) plays a vital role in the pathogenesis of tumors as a competitive endogenous RNA (ceRNA). Given the high aggressiveness and fatality rate of glioblastoma (GBM) as well as poor prognosis, it is necessary to construct a circRNA-related ceRNA network for further studies on the mechanism of GBM and identify possible biomarkers as well as therapeutic drugs.

METHODS

Three datasets from the gene expression omnibus (GEO) database were downloaded to distinguish differential circRNAs, microRNAs, and messenger RNAs respectively in GBM. With the help of GEPIA2, circBank, CSCD, TargetScan, miRDB, and miRTarBase databases, we established a circRNAs-related ceRNA network in GBM. Functional enrichments were employed to profile the most relevant mRNAs to indirectly clarify the mechanisms of the ceRNA network. Based on the expression profile data and survival information of GBM patients from the GEO and the cancer genome atlas (TCGA) databases, we performed survival analysis to select prognostic mRNAs and constructed a novel circRNA-miRNA-mRNA central regulatory subnetwork. The DGIdb database was used to find potential drug-gene interactions.

RESULTS

The datasets obtained from the GEO and TCGA databases were analyzed, and 504 differentially expressed mRNAs (DEmRNAs), 71 differentially expressed microRNAs (DEmiRNAs), and 270 differentially expressed circRNAs (DEcircRNAs) were screened out. The novel ceRNA regulatory network included 22 circRNAs, 11 miRNAs, and 15 mRNAs. FZD1 and KLF10 were significantly correlated with the overall survival rate of patients with GBM (P < 0.05). The final survival subnetwork contained six circRNAs, two miRNAs, and two mRNAs. Two small-molecule compounds and one antibody could be used as therapeutic drugs for GBM. Interestingly, the Wnt signaling pathway appeared in both KEGG and GO functional terms.

CONCLUSIONS

Results of this study demonstrate that FZD1 and KLF10 may exert regulatory functions in GBM, and the ceRNA-mediated network could be a therapeutic strategy for GBM.

MiR-507 inhibits the progression of gastric carcinoma via targeting CBX4-mediated activation of Wnt/β-catenin and HIF-1α pathways

PURPOSE

Gastric carcinoma (GC) is a common malignant disease with high morbidity and mortality. MiR-507 has been confirmed as a tumor inhibitor which can suppress the progression of multiple cancers while its role in GC remains unknown.

METHODS

In this study, the expression levels of miR-507 in the GC tissues and cells were observed by qRT-PCR, and CCK-8 assay, transwell asssay and TUNEL assay were used to observe the function of miR-507 on GC. The miRNA database and dual-luciferase reporter assay were used to investigate the downstream target of miR-507. Moreover, the activities of Wnt/β-catenin and HIF-1α pathways were observed by western blot.

RESULTS

The results showed that miR-507 was significantly downregulated in GC tissues and cell lines, and miR-507 upregulation effectively inhibited the proliferation and invasion and induced the apoptosis of GC cells. CBX4 was a downstream target of miR-507, and CBX4 could reverse the effects of miR-507 on the GC cells. Moreover, it was determined that miR-507 could inhibit CBX4 expression to suppress the activation of Wnt/β-catenin and HIF-1α pathways.

CONCLUSIONS

In conclusion, it suggests that miR-507 could inhibit the progression of GC via regulating CBX4-mediated activation of Wnt/β-catenin and HIF-1α pathways.

Construction of a ceRNA Network and Comprehensive Analysis of lncRNA in Hepatocellular Carcinoma

To explore the RNA biomolecular marker associated with hepatocellular carcinoma (HCC) prognosis, we constructed a regulatory network of competitive endogenous RNAs (ceRNAs), which provides favorable conditions for the early diagnosis, prognostic monitoring, and personalized treatment of HCC. In this study, the differentially expressed genes (DEGs) of patients with HCC were obtained from the Gene Expression Omnibus. We identified 574 upregulated genes and 274 downregulated genes relevant to HCC occurrence (p < 0.05). Subsequently, we constructed the protein−protein interaction (PPI) network using these DEGs and identified the hub genes from the PPI. We then determined the expression and prognostic values of the hub genes from the GEPIA and Kaplan−Meier plotter databases. After the upstream microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were respectively identified by miRTarBase and miRNet, we validated the expression of the key miRNAs in the serum using qPCR experiments. Moreover, we identified a two-lncRNA (LINC01184 and ADORA2A-AS1) signature from the upstream lncRNA that effectively predicted overall survival and had promotive effects for HCC. To verify the clinical significance of the signature, we validated the expression of the lncRNA in HCC tissues. Finally, we discovered and identified four mRNAs, four miRNAs, and five lncRNAs associated with the prognosis of HCC and constructed a new ceRNA regulatory network, which will be beneficial for the accurate diagnosis and treatment of HCC.

Transcriptomic and Metabolomic Profiling in Helicobacter pylori-Induced Gastric Cancer Identified Prognosis- and Immunotherapy-Relevant Gene Signatures

Background: Chronic Helicobacter pylori (HP) infection is considered the major cause of non-cardia gastric cancer (GC). However, how HP infection influences the metabolism and further regulates the progression of GC remains unknown. Methods: We comprehensively evaluated the metabolic pattern of HP-positive (HP+) GC samples using transcriptomic data and correlated these patterns with tumor microenvironment (TME)-infiltrating characteristics. The metabolic score was constructed to quantify metabolic patterns of individual tumors using principal component analysis (PCA) algorithms. The expression alterations of key metabolism-related genes (MRGs) and downstream metabolites were validated by PCR and untargeted metabolomics analysis. Results: Two distinct metabolic patterns and differential metabolic scores were identified in HP+ GC, which had various biological pathways in common and were associated with clinical outcomes. TME-infiltrating profiles under both patterns were highly consistent with the immunophenotype. Furthermore, the analysis indicated that a low metabolic score was correlated with an increased EMT subtype, immunosuppression status, and worse survival. Importantly, we identified that the expression of five MRGs, GSS, GMPPA, OGDH, SGPP2, and PIK3CA, was remarkably correlated with HP infection, patient survival, and therapy response. Furthermore, the carbohydrate metabolism and citric acid may be downstream regulators of the function of metabolic genes in HP-induced GC. Conclusion: Our findings suggest that there is cross talk between metabolism and immune promotion during HP infection. MRG-specific transcriptional alterations may serve as predictive biomarkers of survival outcomes and potential targets for treatment of patients with HP-induced GC.

Essential meiotic structure-specific endonuclease1 (EME1) promotes malignant features in gastric cancer cells via the Akt/GSK3B/CCND1 pathway

DNA damage plays a key role in various biological processes involved in malignant disease, the role of the DNA damage repair gene EME1 (essential meiotic structure-specific endonuclease 1) in gastric cancer (GC) development is unknown. This work aimed to investigate expression and role of EME1 in tumorigenesis. Quantitative real-time polymerase chain reaction (qRT-PCR), immunoblot, cell viability and dual-luciferase reporter assays, RNAi and gene transfection, and immunofluorescent staining were performed to assess EME1 regulation in GC tumorigenesis. Further, mouse xenografts were established for in vivo mechanistic studies. EME1 was found to be upregulated in both gastric cancer cells and clinically obtained tumors. Additionally, EME1 levels were strongly associated with the differentiation level of GC and lymph node metastasis. In vivo and in vitro knockdown of EME1 markedly suppressed the proliferative, migratory, and invasive abilities of GC cells and enhanced apoptotic cell death and cell cycle arrest rates. Mechanistically, EME1 modulated Akt/GSK3B/CCND1 signaling. MYB may also have contributed to EME1-dependent gastric carcinogenesis. Elevated EME1 expressions may enhance the proliferative and metastatic abilities of GC cells, thereby acting as a tumor-promoting factor via Akt. These findings reveal that EME1 is an important biomarker for GC prognosis and treatment in humans.

Abbreviations: Essential meiotic structure-specific endonuclease 1 (EME1); MYB proto-oncogene (MYB); Cell counting kit-8 (CCK-8); 4,6-diamimo-2-phenyl indole (DAPI); Quantitative real-time PCR (qRT-PCR); Gastric cancer (GC); Immunofluorescence (IF); Small interfering RNA (siRNA); Small hairpin RNA (shRNA); Alpha serine threonine-protein kinase (Akt); Glycogen synthase kinase 3 beta (GSK3B); Cyclin D1 (CCND1); Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); Disease-free survival (DFS); Overall survival (OS); Negative controls (NC); American Joint Committee on Cancer (AJCC); Coding sequence (CDS); Lymph node metastasis (LNM); Tris-Buffered Saline-Tween-20 (TBST); Horseradish Peroxidase (HRP); Electrochemiluminescence (ECL); Polyvinylidene Fluoride (PVDF); Excision repair cross complementation group 1 (ERCC1).

An Advanced Systems Pharmacology Strategy Reveals AKR1B1, MMP2, PTGER3 as Key Genes in the Competing Endogenous RNA Network of Compound Kushen Injection Treating Gastric Carcinoma by Integrated Bioinformatics and Experimental Verification

Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by in vivo and in vitro experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as AKR1B1, MMP2 and PTGERR3. In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.

lncRNA DLEU2 promotes gastric cancer progression through ETS2 via targeting miR-30a-5p

Background

Gastric cancer (GC) remains an important cancer worldwide. Further understanding of the molecular mechanisms of gastric carcinogenesis will enhance the diagnosis and treatment of GC.

Methods

The expression of DLEU2 and ETS2 was analyzed in several GC cell lines using GEPIA online analyze, qRT-PCR and immunohistochemistry. The biological behavior of GC cells was detected by CCK8, clone formation, transwell, wound healing, western blot, and flow cytometry assay. More in-depth mechanisms were studied.

Results

DLEU2 was significantly up-regulated in GC tissues and cell lines. The expression of DLEU2 was significantly associated with pathological grading and TNM stage of GC patients. Furthermore, knockdown of DLEU2 inhibited the proliferation, migration, and invasion of AGS and MKN-45 cells, while overexpression of DLEU2 promoted the proliferation, migration, and invasion of HGC-27 cells. MiR-30a-5p could directly bind to the 3’ UTR region of ETS2. Moreover, DLEU2 bound to miR-30a-5p through the same binding site, which facilitated the expression of ETS2. Knockdown of DLEU2 reduced the protein level of intracellular ETS2 and inhibited AKT phosphorylation, while overexpression of DLEU2 induced the expression of ETS2 and the phosphorylation of AKT. ETS2 was highly expressed in GC tissues. The expression of ETS2 was significantly associated with age, pathological grading, and TNM stage. ETS2 overexpression promoted cell proliferation and migration of AGS and MKN-45 cells. Furthermore, ETS2 overexpression rescued cell proliferation and migration inhibition induced by DLEU2 down-regulation and miR-30a-5p up-regulation in AGS and MKN-45 cells.

Conclusions

DLEU2 is a potential molecular target for GC treatment.

Comprehensive analysis of lncRNA-miRNA-mRNA regulatory networks for microbiota-mediated colorectal cancer associated with immune cell infiltration

Recent findings have identified microbiota as crucial participants in many disease conditions, including cancers. Competing endogenous RNA (ceRNA) is regarded as a candidate mechanism involving relevant biological processes. We therefore constructed a ceRNA network using the TCGA and GEO database, to determine the potential mechanisms of microbiota-mediated colorectal carcinogenesis and progression. We found a total of 75 lncRNAs, 8 miRNAs, and 9 mRNAs in the probiotics-mediated ceRNA network and a total of 49 lncRNAs, 4 miRNAs, and 3 mRNA in the pathobiont-mediated ceRNA network, which could induce the microbiota-mediated carcinogenesis and progression. The GO and KEGG analysis indicated that the ceRNA network is mainly enriched in the metabolic process, and two unique pathways (the p53 signaling pathway and microRNA in cancer), respectively. A four-gene signature (FRMD6-AS2, DIRC3, LIFR-AS1, and MRPL23-AS1) was suggested as an independent prognostic factor. Four lncRNAs (LINC00355, KCNQ1OT1, LINC00491, and HOTAIR) were associated with poor survival. Three small molecule candidate anticancer drugs (Pentoxyverine, Rimexolone, and Doxylamine) were identified. A four-gene signature (FAM129A, BCL2, PMAIP1, and RPS6) is significantly correlated with immune infiltration level. This study provides a promising biomarker reservoir to explore the mechanism by which microbiota regulate the ceRNA network involving the immune response, and further participate in colorectal carcinogenesis and progression.

Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Comprehensive Analysis of the Immune-Oncology Targets and Immune Infiltrates of N 6-Methyladenosine-Related Long Noncoding RNA Regulators in Breast Cancer

Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N⁶-methyladenosine-related long non-coding RNAs (m⁶A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson’s correlation analysis, and univariate and multivariate Cox regression were performed to select m⁶A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m⁶A-related lncRNA prognostic signature (m⁶A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m⁶A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m⁶A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.

Construction of a novel mRNA-miRNA-lncRNA network and identification of potential regulatory axis associated with prognosis in colorectal cancer liver metastases

Liver metastasis is a leading cause of death in patients with colorectal cancer (CRC). Increasing evidence demonstrates that competing endogenous RNA (ceRNA) networks play important roles in malignant cancers. The purpose of this study was to identify molecular markers and build a ceRNA network as a significant predictor of colorectal liver metastases (CRLM). By integrated bioinformatics analysis, we found that apolipoprotein C1 (APOC1) was upregulated in CRLM and associated with prognosis in patients with CRC and thereby established an APOC1-dependent ceRNA network. By survival analysis, expression analysis, and correlation analysis of each element in the ceRNA network, we identified that ZEB1-AS1, miR-335-5p and APOC1 regulated each other. We further experimentally confirmed that ZEB1-AS1 promoted a CRC progression via regulating the expression of miR-335-5p that controlled the expression of APOC1. Our findings indicate that the ZEB1-AS1-miR-335-5p-APOC1 ceRNA regulatory network is significantly valuable for better prognosis of patients with CRC and as a new therapeutic target for the treatment of CRLM.

High expression of TREM2 promotes EMT via the PI3K/AKT pathway in gastric cancer: bioinformatics analysis and experimental verification

Background: To date, the pathogenesis of gastric cancer (GC) remains unclear. We combined public database resources and bioinformatics analysis methods, explored some novel genes and verified the experiments to further understand the pathogenesis of GC and to provide a promising target for anti-tumor therapy. Methods: We downloaded the chip data related to GC from the Gene Expression Omnibus (GEO) database, extracted differentially expressed genes (DEGs), and then determined the key genes in the development of GC via PPI networks and model analysis. Functional annotation via GO and KEGG enrichment of DEGs was used to understand the latent roles of DEGs. The expression of the triggering receptor expressed on myeloid cells 2 (TREM2) gene in GC cell lines was verified via RT-PCR and western blotting. Moreover, the CCK-8, wound healing assay, and transwell migration and invasion assays were used to understand the changes in the proliferation, migration, and invasion abilities of GC cells after silencing TREM2. Western blotting verified the interaction between TREM2 and PI3K predict of the string website, as well as the effect of TREM2 on EMT. Finally, a lung metastasis model was used to explore the relationship between TREM2 and metastasis. Results: Our study identified 16 key genes, namely BGN, COL1A1, COL4A1, COL5A2, NOX4, SPARC, HEYL, SPP1, TIMP1, CTHRC1, TREM2, SFRP4, FBXO32, GPX3, KIF4A, and MMP9 genes associated with GC. The EMT-related pathway was the most significantly altered pathway. TREM2 expression was higher in GC cell lines and was remarkably associated with tumor invasion depth, TNM stage, histological grade, histological type, anatomic subdivision, and Helicobacter pylori state. Knockdown of TREM2 expression inhibited the proliferation, migration, and invasion of GC cells as well as the progression of EMT by PI3K/AKT signaling in vitro. In addition, lung metastasis were decreased in vivo. Conclusions: We identified some important genes associated with the progression of GC via public database analysis, explored and verified the effects of proto-oncogene TREM2 on EMT via the PI3K/AKT pathway. TREM2 may be a novel target in the GC therapy.

Genomic analysis and clinical implications of immune cell infiltration in gastric cancer

The immune infiltration of patients with gastric cancer (GC) is closely associated with clinical prognosis. However, previous studies failed to explain the different subsets of immune cells involved in immune responses and diverse functions. The present study aimed to uncover the differences in immunophenotypes in a tumor microenvironment (TME) between adjacent and tumor tissues and to explore their therapeutic targets. In our study, the relative proportion of immune cells in 229 GC tumor samples and 22 paired matched tissues was evaluated with a Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithm. The correlation between immune cell infiltration and clinical information was analyzed. The proportion of 22 immune cell subsets was assessed to determine the correlation between each immune cell type and clinical features. Three molecular subtypes were identified with ‘CancerSubtypes’ R-package. Functional enrichment was analyzed in each subtype. The profiles of immune infiltration in the GC cohort from The Cancer Genome Atlas (TCGA) varied significantly between the 22 paired tissues. TNM stage was associated with M1 macrophages and eosinophils. Follicular helper T cells were activated at the late stage. Monocytes were associated with radiation therapy. Three clustering processes were obtained via the ‘CancerSubtypes’ R-package. Each cancer subtype had a specific molecular classification and subtype-specific characterization. These findings showed that the CIBERSOFT algorithm could be used to detect differences in the composition of immune-infiltrating cells in GC samples, and these differences might be an important driver of GC progression and treatment response.

Competing Endogenous RNA Networks as Biomarkers in Neurodegenerative Diseases

Protein aggregation is classically considered the main cause of neuronal death in neurodegenerative diseases (NDDs). However, increasing evidence suggests that alteration of RNA metabolism is a key factor in the etiopathogenesis of these complex disorders. Non-coding RNAs are the major contributor to the human transcriptome and are particularly abundant in the central nervous system, where they have been proposed to be involved in the onset and development of NDDs. Interestingly, some ncRNAs (such as lncRNAs, circRNAs and pseudogenes) share a common functionality in their ability to regulate gene expression by modulating miRNAs in a phenomenon known as the competing endogenous RNA mechanism. Moreover, ncRNAs are found in body fluids where their presence and concentration could serve as potential non-invasive biomarkers of NDDs. In this review, we summarize the ceRNA networks described in Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis and spinocerebellar ataxia type 7, and discuss their potential as biomarkers of these NDDs. Although numerous studies have been carried out, further research is needed to validate these complex interactions between RNAs and the alterations in RNA editing that could provide specific ceRNET profiles for neurodegenerative disorders, paving the way to a better understanding of these diseases.

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

Background

Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.

Methods

The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram.

Results

Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally.

Conclusion

Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

Identification of key long non-coding RNAs in gastric adenocarcinoma

BACKGROUND

Gastric cancer is the third leading cause of cancer-related deaths worldwide.

OBJECTIVE

The present study aims to identify key long non-coding RNAs (lncRNAs) and their potential roles in the pathogenesis of gastric adenocarcinoma.

METHODS

The lncRNA and mRNA expression profile between gastric adenocarcinoma and adjacent non-tumor tissues were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between gastric adenocarcinoma and adjacent non-tumor tissues were identified after bioinformatics analysis. DElncRNA-DEmRNA co-expression network and DElncRNA-nearby DEmRNA interaction network were constructed, respectively. Functional annotation for DEmRNAs interacted with DElncRNAs was performed. Receiver operating characteristic (ROC) analysis of selected DElncRNAs was conducted.

RESULTS

Based on TCGA, the mRNA and lncRNA expression profiles of 375 gastric adenocarcinoma and 32 adjacent non-tumor tissues were downloaded. A total of 1502 DEmRNAs and 928 DElncRNAs between gastric adenocarcinoma and adjacent non-tumor tissues were identified. HOXC-AS3 might involve with gastric adenocarcinoma by regulating a set of HOX genes (HOXC8, HOXC9, HOXC10, HOXC11, HOXC12 and HOXC13) with cis-effect. AC115619.1-APOA4/APOB and AP006216.2-APOA1/APOA4 integrations might play roles in gastric adenocarcinoma through regulating pathways of Fat digestion and absorption and Vitamin digestion and absorption. Six lncRNAs including (HOTAIR, C20orf166-AS1, PGM5-AS1, HOXC-AS3, HOXC-AS2 and AC012531.1) have excellent diagnostic value for gastric adenocarcinoma.

CONCLUSIONS

This study identifies key lncRNAs in gastric adenocarcinoma which provides clues for exploring the pathogenesis and developing potential biomarkers for gastric adenocarcinoma.

Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA-miRNA-mRNA networks and Cox regression models

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide, and its underlying mechanism remains unclear. Accumulating evidence has highlighted that long non-coding RNA (lncRNA) acts as competitive endogenous RNA (ceRNA) and plays an important role in the occurrence and development of LUAD. Here, we comprehensively analyzed and provided an overview of the lncRNAs, miRNAs, and mRNAs associated with LUAD from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA) were used to construct a lncRNA–miRNA–mRNA regulatory network according to interaction information from miRcode, TargetScan, miRTarBase, and miRDB. Finally, the RNAs of the network were analyzed for survival and submitted for Cox regression analysis to construct prognostic indicators. A total of 1123 DElncRNAs, 95 DEmiRNAs, and 2296 DEmRNAs were identified (|log₂FoldChange| (FC) > 2 and false discovery rate (FDR) or adjusted P value < 0.01). The ceRNA network was established based on this and included 102 lncRNAs, 19 miRNAs, and 33 mRNAs. The DEmRNAs in the ceRNA network were found to be enriched in various cancer-related biological processes and pathways. We detected 22 lncRNAs, 12 mRNAs, and 1 miRNA in the ceRNA network that were significantly associated with the overall survival of patients with LUAD (P < 0.05). We established three prognostic prediction models and calculated the area under the 1,3,5-year curve (AUC) values of lncRNA, mRNA, and miRNA, respectively. Among them, the prognostic index (PI) of lncRNA showed good predictive ability which was 0.737, 0.702 and 0.671 respectively, and eight lncRNAs can be used as candidate prognostic biomarkers for LUAD. In conclusion, our study provides a new perspective on the prognosis and diagnosis of LUAD on a genome-wide basis, and develops independent prognostic biomarkers for LUAD.

Role of hypoxia inducible factor-1 in cancer stem cells (Review)

Cancer stem cells (CSCs) have been found to play a decisive role in cancer recurrence, metastasis, and chemo‑, radio‑ and immuno‑resistance. Understanding the mechanism of CSC self‑renewal and proliferation may help overcome the limitations of clinical treatment. The microenvironment of tumor growth consists of a lack of oxygen, and hypoxia has been confirmed to induce cancer cell invasion, metastasis and epithelial‑mesenchymal transition, and is usually associated with poor prognosis and low survival rates. Hypoxia inducible factor‑1 (HIF‑1) can be stably expressed under hypoxia and act as an important molecule to regulate the development of CSCs, but the specific mechanism remains unclear. The present review attempted to explain the role of HIF‑1 in the generation and maintenance of CSCs from the perspective of epigenetics, metabolic reprogramming, tumor immunity, CSC markers, non‑coding RNA and signaling pathways associated with HIF‑1, in order to provide novel targets with HIF‑1 as the core for clinical treatment, and extend the life of patients.

13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC.

METHODS

The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis.

RESULTS

We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways.

CONCLUSION

In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.

Non‑coding RNAs that regulate the Wnt/β‑catenin signaling pathway in gastric cancer: Good cop, bad cop? (Review)

Gastric cancer (GC) is one of the most common causes of cancer‑related mortality worldwide. Despite remarkable progress in the diagnosis and treatment of GC, a large number of cases are diagnosed as advanced GC, and treatment failure occurs. Emerging evidence has shown that non‑coding RNAs (ncRNAs), especially microRNAs (miRNAs) and long non‑coding RNAs (lncRNAs), play a vital role in the tumorigenesis and development of GC. Moreover, the pathogenesis of GC is closely related to aberrant activation of the Wnt (Wingless‑type MMTV integration site family) signaling pathway. ncRNAs serve as potential novel biomarkers in the clinical examination, prognosis and therapeutic targeting of GC. Furthermore, dysregulation of ncRNAs has been demonstrated to affect tumor initiation, epithelial‑mesenchymal transition (EMT), angiogenesis, tumor development, invasion, metastasis and resistance to therapy via the Wnt/β‑catenin signaling pathway. This review focuses on the role of ncRNAs in modulating the Wnt/β‑catenin signaling pathway in the pathogenesis of GC, which may provide a reference for the clinical diagnosis and treatment of GC.

Gastric cancer risk-scoring system based on analysis of a competing endogenous RNA network

Background

Long noncoding RNAs (lncRNAs) can play vital roles in tumor initiation, progression, invasion, and metastasis. However, the functional role of the lncRNA-based competing endogenous RNA (ceRNA) networks in gastric cancer (GC) is still unclear. We aimed to identify novel lncRNAs and their association with GC prognosis.

Methods

The lncRNA, miRNA, and mRNA expression profiles of GC patients data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using the edge-R package. Then, the relationship among lncRNAs-miRNAs-mRNAs was integrated into a constructed ceRNA network with Cytoscape software. Using Cox regression analysis, a risk score system based on DEGs associated with patient prognosis in GC was established. Finally, a nomogram was founded to predict the prognosis of GC patients.

Results

A total of 971 differentially expressed lncRNAs (DElncRNAs), 144 differentially expressed miRNAs (DEmiRNAs) and 2,789 differentially expressed mRNAs (DEmRNAs) were identified and found to be associated with GC risk. Using the bioinformatics method, a ceRNA network involving 62 DElncRNAs, 21 DEmiRNAs and 59 DEmRNAs was constructed. Based on the results of the Cox regression analysis, a risk-scoring system involving 3 lncRNAs (i.e., ADAMTS9-AS1, C15orf54, and AL391152.1) was set up for the survival analysis of GC patients. The area under the receiver operating characteristic (ROC) curve for the risk-scoring system was 0.674, with a C-index of 0.64 [95% confidence interval (CI): 0.59–0.69, P=2.806485e−08]. Univariate and multivariate Cox regression analyses demonstrated that the risk-scoring system was an independent prognostic factor for GC. The risk-scoring system is positively associated with advanced tumor grade. The expression of these 3 lncRNAs were validated in GEPIA database. A nomogram based on these 3 lncRNAs was created to predict the prognosis of GC patients.

Conclusions

Our study established a novel lncRNA-expression-based ceRNA network and an ADAMTS9-AS1-C15orf54-AL391152.1-based risk-scoring system, which can be used to predict the prognosis of GC patients.

Construction of lncRNA-associated ceRNA networks to identify prognostic lncRNA biomarkers for glioblastoma

Long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) that play significant regulatory roles in the pathogenesis of tumors. However, the role of lncRNAs, especially the lncRNA-related ceRNA regulatory network, in glioblastoma (GBM) has not been fully elucidated. The goal of the current study was to construct lncRNA-microRNA-mRNA-related ceRNA networks for further investigation of their mechanism of action in GBM. We downloaded data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases and identified differential lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) associated with GBM. A ceRNA network was constructed and analyzed to examine the relationship between lncRNAs and patients' overall survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were used to analyze the related mRNAs to indirectly explain the mechanism of action of lncRNAs. The potential effective drugs for the treatment of GBM were identified using the connectivity map (CMap). After integrated analysis, we obtained a total of 210 differentially expressed lncRNAs, 90 differentially expressed miRNAs, and 2508 differentially expressed mRNAs (DEmRNAs) from the TCGA and GEO databases. Using these differential genes, we constructed a lncRNA-associated ceRNA network. Six lncRNAs in the ceRNA network were associated with the overall survival of patients with GBM. Through KEGG analysis, it was found that the DEmRNAs involved in the network are related to cancer-associated pathways, for instance, mitogen-activated protein kinase and Ras signaling pathways. CMap analysis revealed four small-molecule compounds that could be used as drugs for the treatment of GBM. In this study, a multi-database joint analysis was used to construct a lncRNA-related ceRNA network to help identify the regulatory functions of lncRNAs in the pathogenesis of GBM.

The construction and analysis of competitive endogenous RNA (ceRNA) networks in metastatic renal cell carcinoma: a study based on The Cancer Genome Atlas

Background

The pathogenesis of metastatic renal cell carcinoma (mRCC), one of the most common malignant neoplasms, remains unknown. Studies on competitive endogenous RNAs (ceRNAs) have uncovered new interactions between RNAs, which suggest their roles in cancer pathogenesis. However, the role of ceRNA networks in mRCC has not yet been studied. Thus, this study aims to explore the role of ceRNA networks in mRCC development and identify potential prognostic indicators.

Methods

We analyzed RNA sequencing data of mRCC patients, which had been obtained from The Cancer Genome Atlas (TCGA) database. Next, differentially expressed long-noncoding RNAs (DElncRNAs), differentially expressed micro RNAs (DEmiRNAs), and differentially expressed messenger RNAs (DEmRNAs) in mRCC and clear cell RCC (ccRCC) samples were identified using the edgeR package that is available in R software. Moreover, based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID), enrichment analyses for biological processes and pathways functional were performed. As such, we built a ceRNA network and performed a survival analysis using the survival package in R.

Results

A total of 369 DElncRNAs, 12 DEmiRNAs, and 728 DEmRNAs were identified for further analysis. Of these, 11 lncRNAs, 20 mRNAs, and 2 miRNAs were included in the ceRNA network. Moreover, 7 of the 11 lncRNAs and 3 of the 20 mRNAs were associated with the overall survival of mRCC patients (P<0.05).

Conclusions

Collectively, our findings allow a deepened understanding of the molecular mechanism of the ceRNA network and its role in mRCC development, which can guide both mRCC therapy and related future research.

Downregulated long noncoding RNA LINC00313 inhibits the epithelial-mesenchymal transition, invasion, and migration of thyroid cancer cells through inhibiting the methylation of ALX4

Thyroid cancer represents one of the prevalent endocrine cancer with relatively high incidence rate around the world, accompanied by unchanged fatality rate. We probe into the specific role of LINC00313 in mediation of cellular processes of thyroid cancer including proliferation, migration, and invasion through the methylation of aristaless-like homeobox 4 (ALX4). Thyroid cancer-related long noncoding RNAs (lncRNAs) and genes were analyzed by microarray-based analysis. The antitumor effect of LINC00313 was examined with the gain- and loss-of-function experiments. In addition, the binding of LINC00313 and the promoter region of ALX4, and the interaction of LINC00313 with methylation-related proteins were detected. Later, xenograft tumors in nude mice were induced expecting to dig out the modulatory function of LINC00313 in tumor growth of thyroid carcinoma. The microarray-based analysis manifested that LINC00313 was overexpressed, whereas ALX4 was downregulated in thyroid cancer, the results of which were also verified in thyroid cancer tissues. Besides, our results demonstrated that LINC00313 bound to the ALX4 promoter region, and LINC00313 recruited DNMT1 and DNMT3B proteins to promote the methylation of ALX4 promoter region, thus suppressing the ALX4 expression. Finally, the downregulation of LINC00313 and upregulation of ALX4 repressed the AKT/mTOR signaling axis, thus inhibiting proliferative, migratory, invasive abilities as well as epithelial-to-mesenchymal transition (EMT) of thyroid cancer cells. Collectively, downregulated LINC00313 suppresses cell proliferation, migration, as well as invasion of thyroid cancer by inhibiting the methylation of ALX4 and increasing its expression by inactivation of the AKT/mTOR signaling pathway.

Long non-coding RNA CASC19 is associated with the progression and prognosis of advanced gastric cancer

Evidence indicates that aberrantly expressed long non-coding RNAs (lncRNAs) are involved in the development and progression of advanced gastric cancer (AGC). Using RNA sequencing data and clinical information obtained from The Cancer Gene Atlas, we combined differential lncRNA expression profiling and weighted gene co-expression network analysis to identify key lncRNAs associated with AGC progression and prognosis. Cancer susceptibility 19 (CASC19) was the top hub lncRNA among the lncRNAs included in the gene module most significantly correlated with AGC’s pathological variables. CASC19 was upregulated in AGC clinical samples and was significantly associated with higher pathologic TNM stage, pathologic T stage, lymph node metastasis, and poor overall survival. Multivariable Cox analysis confirmed that CASC19 overexpression is an independent prognostic factor for overall survival. Furthermore, quantitative real-time PCR assay confirmed that CASC19 expression in four human gastric cancer cells (AGS, BGC-823, MGC-803, and HGC-27) was significantly upregulated compared with human normal gastric mucosal epithelial cell line (GES-1). Functionally, CASC19 knockdown inhibited GC cell proliferation and migration in vitro. These findings suggest that CASC19 may be a novel prognostic biomarker and a potential therapeutic target for AGC.

Differential Analysis of lncRNA, miRNA and mRNA Expression Profiles and the Prognostic Value of lncRNA in Esophageal Cancer

Integrative central axis of lncRNA-miRNA-mRNA plays pivotal roles in tumor development and progression. However, the regulatory role of lncRNA-miRNA-mRNA in esophageal cancer remains elusive. TCGA database was utilized to investigate the differential expression of lncRNA, miRNA and mRNA in esophageal cancer (ESCA) and normal esophageal tissues, and GEO database was used to further validate the expression profile of key genes. Differential lncRNAs in TCGA database were submitted to Starbase, and lncRNAs related to overall survival were analyzed using Kaplan-Meier and log-rank test. We found 145 lncRNAs, 112 miRNAs and 2000 protein coding mRNAs were differentially expressed in ESCA samples, which were tightly involved in chromosome segregation, extracellular matrix assembly by GO assay, and KEGG assay revealed the correlation of differentially expressed genes with cell cycle, apoptosis and cGMP-PKG signaling pathway. Furthermore, there were 291 nodes in ceRNA network, which consisted of 40 lncRNAs, 28 miRNAs and 233 mRNAs, and formed 677 relations. Furthermore, 6 of 10 lncRNAs in TCGA database were consistent with GEO database, and expressions of 10 mRNAs in TCGA database all exhibited the same tendency with GEO database. Notably, we found 8 lncRNAs (WDFY3-AS2, CASC8, UGDH-AS1, RAP2C-AS1, AC007128.1, AC016205.1, AC092803.2 and AC079949.2) were correlated with overall survival of the patients with ESCA. The key differentially expressed genes participate in the development and progression of ESCA, and thus the elucidation of functions of lncRNA-miRNA-mRNA will provide new novel therapeutic target for the patients with ESCA.

A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma

Background

Cholangiocarcinoma (CCA) is the second most common malignant primary liver tumor and has shown an alarming increase in incidence over the last two decades. However, the mechanisms behind tumorigenesis and progression remain insufficient. The present study aimed to uncover the underlying regulatory mechanism on CCA and find novel biomarkers for the disease prognosis.

Method

The RNA-sequencing (RNA-seq) datasets of lncRNAs, miRNAs, and mRNAs in CCA as well as relevant clinical information were obtained from the Cancer Genome Atlas (TCGA) database. After pretreatment, differentially expressed RNAs (DERNAs) were identified and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Then, a ceRNA network was constructed based on these RNAs.

Results

We identified a total of five prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues. Integrating the relationship between the different types of RNAs, an lncRNA-miRNA-mRNA network was established and included 28 molecules and 47 interactions. Screened prognostic RNAs involved in the ceRNA network included 3 miRNAs (hsa-mir-1295b, hsa-mir-33b, and hsa-mir-6715a), 7 lncRNAs (ENSG00000271133, ENSG00000233834, ENSG00000276791, ENSG00000241155, COL18A1-AS1, ENSG00000274737, and ENSG00000235052), and 18 mRNAs (ANO9, FUT4, MLLT3, ABCA3, FSCN2, GRID2IP, NCK2, MACC1, SLC35E4, ST14, SH2D3A, MOB3B, ACTL10, RAB36, ATP1B3, MST1R, SEMA6A, and SEL1L3).

Conclusions

Our study identified novel prognostic makers and predicted a previously unknown ceRNA regulatory network in CCA and may provide novel insight into a further understanding of lncRNA-mediated ceRNA regulatory mechanisms in CCA.

Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.

METHODS

Integrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.

RESULTS

A total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.

CONCLUSION

This study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.